Modern database systems rely on workload forecasting to guide performance tuning, resource allocation, and query optimization. However, evolving query patterns and resource demands make accurate workload forecasting increasingly challenging. Most existing methods separately model query and resource behaviors using simple statistics or single-scale forecasting models, thereby ignoring their inherent correlations and leading to inaccurate modeling of real-world workload behaviors. In practice, real-world workloads show that query and resource behaviors are often interdependent and exhibit temporal dynamics. However, most forecasting methods overlook the inherent correlations between query and resource, as well as the temporal patterns across multi-scale resolutions in real workloads. To address these issues, we propose QuadraFormer, an end-to-end forecasting framework that jointly predicts query and resource usage. First, we introduce a unified workload representation that integrates query and resource information into a structured input, enabling joint modeling of workload across variates. Second, we develop an adaptive multi-scale routing mechanism that selects appropriate temporal resolutions to capture periodic patterns and multi-scale variability. Third, we design a novel Quadra-attention mechanism that models both intra- and inter-patch temporal dependencies as well as intra- and inter-dimensional dependencies. Extensive experiments on real-world workloads show that QuadraFormer consistently outperforms competitive baselines, achieving the highest F1 score of 98.38% and accuracy of 86.17%, with F1 improvements of up to 10.28% over the best-performing baseline, while reducing training time by up to 2.16×.

Multiview clustering has aroused increasing attention in recent years since real-world data are always comprised of multiple features or views. Despite the existing clustering methods having achieved promising performance, there still remain some challenges to be solved: 1) most existing methods are unscalable to large-scale datasets due to the high computational burden of eigendecomposition or graph construction and 2) most methods learn latent representations and cluster structures separately. Such a two-step learning scheme neglects the correlation between the two learning stages and may obtain a suboptimal clustering result. To address these challenges, a pseudo-label guided collective matrix factorization (PLCMF) method that jointly learns latent representations and cluster structures is proposed in this article. The proposed PLCMF first performs clustering on each view separately to obtain pseudo-labels that reflect the intraview similarities of each view. Then, it adds a pseudo-label constraint on collective matrix factorization to learn unified latent representations, which preserve the intraview and interview similarities simultaneously. Finally, it intuitively incorporates latent representation learning and cluster structure learning into a joint framework to directly obtain clustering results. Besides, the weight of each view is learned adaptively according to data distribution in the joint framework. In particular, the joint learning problem can be solved with an efficient iterative updating method with linear complexity. Extensive experiments on six benchmark datasets indicate the superiority of the proposed method over state-of-the-art multiview clustering methods in both clustering accuracy and computational efficiency.